Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Abstract Motivation Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the...

Full description

Saved in:
Bibliographic Details
Published in:Bioinformatics (Oxford, England) Vol. 39; no. 12
Main Authors: Díaz-Gay, Marcos, Vangara, Raviteja, Barnes, Mark, Wang, Xi, Islam, S M Ashiqul, Vermes, Ian, Duke, Stephen, Narasimman, Nithish Bharadhwaj, Yang, Ting, Jiang, Zichen, Moody, Sarah, Senkin, Sergey, Brennan, Paul, Stratton, Michael R, Alexandrov, Ludmil B
Format: Journal Article
Language:English
Published: England Oxford University Press 01.12.2023
Oxford Publishing Limited (England)
Subjects:
Algorithms
Availability
Cancer
Computer applications
Digital signatures
Genome
Genomes
Humans
Mutation
Neoplasms - genetics
Software
Statistical analysis
ISSN:1367-4811, 1367-4803, 1367-4811
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Motivation Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. Results Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. Availability and implementation SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.
Bibliography:SourceType-Scholarly Journals-1
content type line 14
ObjectType-Report-1
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad756